#MNIST数据集下的参数
attack_config = {
    'BIM': {
        'epsilon':0.3,'eps_iter':0.5,'num_steps':10
    },
    'BLB': {
        'second_layer_loop':5,'init_const':1e-2,'max_iterations':1000
    },
    'CW2': {
        'learning_rate':0.02,'lower_bound':0.0,'upper_bound':1.0,'second_layer_loop':5,'init_const':1e-3,'max_iterations':1000,'kappa':0 * 1.0
    },
    'DEEPFOOL':{
        'overshoot':0.02,'max_iterations':50
    },
    'EAD': {
        'learning_rate':0.02,'lower_bound':0.0,'upper_bound':1.0,'second_layer_loop':5,'init_const':1e-3,'max_iterations':1000,'kappa':0 * 1.0,
        'beta':1e-3
    },
    'FGSM': {
        'epsilon': 0.3
    },
    'ILLC': {
        'epsilon': 0.9,'eps_iter':0.5,'num_steps':10
    },
    'JSMA':{
        'theta':1.0,'gamma':0.1
    },
    'LLC': {
        'epsilon': 0.9
    },
    'OPA':{
        'pixels':1,'popsize':400,'maxiter':75
    },
    'OPTMARGIN': {
        'learning_rate':0.02,'lower_bound':0.0,'upper_bound':1.0,'second_layer_loop':5,'init_const':1e-3,'max_iterations':100,'kappa':0 * 1.0,
        'noise_count':10,'noise_magnitude':0.3
    },
    'PGD':{
        'epsilon': 0.3,'eps_iter':0.05,'num_steps':15
    },
    'R+FGSM': {
        'epsilon': 0.3,'alpha':0.5
    },
    'R+LLC': {
        'epsilon': 0.3,'alpha':0.5
    },
    'UAP':{
        'max_iter_universal':5,'fooling_rate':1.0,'epsilon':0.1,'max_iterations':10,'overshoot':0.02,
        'max_iter_deepfool':5
    },
    'UMI+FGSM': {
        'epsilon': 0.3,'eps_iter':0.05,'num_steps':15,'decay_factor':1.0
    },
    'TMI+FGSM': {
        'epsilon': 0.3,'eps_iter':0.05,'num_steps':15,'decay_factor':1.0
    },
}


#CIFAR10数据集下的参数（如需要使用请把MNIST数据集下的参数注释并去掉本部分的注释）
'''attack_config = {
    'BIM': {
        'epsilon':0.1,'eps_iter':0.05,'num_steps':15
    },
    'BLB': {
        'second_layer_loop':5,'init_const':1e-2,'max_iterations':1000
    },
    'CW2': {
        'learning_rate':0.02,'lower_bound':0.0,'upper_bound':1.0,'second_layer_loop':5,'init_const':1e-3,'max_iterations':1000,'kappa':0 * 1.0
    },
    'DEEPFOOL':{
        'overshoot':0.02,'max_iterations':50
    },
    'EAD': {
        'learning_rate':0.02,'lower_bound':0.0,'upper_bound':1.0,'second_layer_loop':5,'init_const':1e-3,'max_iterations':1000,'kappa':0 * 1.0,
        'beta':1e-3
    },
    'FGSM': {
        'epsilon': 0.1
    },
    'ILLC': {
        'epsilon': 0.1,'eps_iter':0.05,'num_steps':10
    },
    'JSMA':{
        'theta':1.0,'gamma':0.1
    },
    'LLC': {
        'epsilon': 0.1
    },
    'OPA':{
        'pixels':1,'popsize':400,'maxiter':75
    },
    'OPTMARGIN': {
        'learning_rate':0.02,'lower_bound':0.0,'upper_bound':1.0,'second_layer_loop':5,'init_const':1e-3,'max_iterations':100,'kappa':0 * 1.0,
        'noise_count':10,'noise_magnitude':0.3
    },
    'PGD':{
        'epsilon': 0.1,'eps_iter':0.05,'num_steps':10
    },
    'R+FGSM': {
        'epsilon': 0.1,'alpha':0.01
    },
    'R+LLC': {
        'epsilon': 0.1,'alpha':0.15
    },
    'UAP':{
        'max_iter_universal':5,'fooling_rate':1.0,'epsilon':0.1,'max_iterations':10,'overshoot':0.02,
        'max_iter_deepfool':5
    },
    'UMI+FGSM': {
        'epsilon': 0.1,'eps_iter':0.05,'num_steps':10,'decay_factor':1.0
    },
    'TMI+FGSM': {
        'epsilon': 0.1,'eps_iter':0.05,'num_steps':10,'decay_factor':1.0
    },
}'''
